






"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor




class 						__UpperCamelCase     (     unittest.TestCase						):
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Optional[int]=7     , UpperCAmelCase							:	int=3     , UpperCAmelCase							:	Optional[Any]=18     , UpperCAmelCase							:	Optional[Any]=30     , UpperCAmelCase							:	List[str]=400     , UpperCAmelCase							:	Optional[Any]=True     , UpperCAmelCase							:	Dict=32     , UpperCAmelCase							:	Optional[int]=True     , )	->       Dict:
											lowerCAmelCase   :Optional[int]						=			parent
											lowerCAmelCase   :Tuple						=			batch_size
											lowerCAmelCase   :Optional[Any]						=			num_channels
											lowerCAmelCase   :Optional[int]						=			image_size
											lowerCAmelCase   :Optional[int]						=			min_resolution
											lowerCAmelCase   :Optional[int]						=			max_resolution
											lowerCAmelCase   :Tuple						=			do_resize
											lowerCAmelCase   :Optional[int]						=			size_divisor
											lowerCAmelCase   :Optional[int]						=			do_rescale
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       int:
											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}




@require_torch
@require_vision
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Any      =  GLPNImageProcessor if is_vision_available() else None
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			GLPNImageProcessingTester(self   )
					@property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       str:
											return self.image_processor_tester.prepare_image_processor_dict()
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Optional[int]:
											lowerCAmelCase   :str						=			self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_resize'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'size_divisor'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'resample'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_rescale'   )   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Optional[Any]:
											pass
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											# Initialize image_processing
											lowerCAmelCase   :Dict						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											lowerCAmelCase   :Any						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , Image.Image   )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   :str						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Tuple:
											# Initialize image_processing
											lowerCAmelCase   :Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											lowerCAmelCase   :Optional[int]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , numpify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , np.ndarray   )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   :List[Any]						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[str]:
											# Initialize image_processing
											lowerCAmelCase   :Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											lowerCAmelCase   :Optional[Any]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , torchify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , torch.Tensor   )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   :Tuple						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"